
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_ 				=			logging.get_logger(__name__)


def 							a__					(							snake_case							):





  """simple docstring"""
  # initialize config
  if "resnet-50" in model_name:
    __SCREAMING_SNAKE_CASE      :	Any					=		ResNetConfig.from_pretrained('''microsoft/resnet-50'''							)
  elif "resnet-101" in model_name:
    __SCREAMING_SNAKE_CASE      :	Any					=		ResNetConfig.from_pretrained('''microsoft/resnet-101'''							)
  else:
    raise ValueError('''Model name should include either resnet50 or resnet101'''							)

  __SCREAMING_SNAKE_CASE      :	List[str]					=		DetrConfig(use_timm_backbone=snake_case       ,			backbone_config=snake_case							)

  # set label attributes
  __SCREAMING_SNAKE_CASE      :	Dict					=		'''panoptic''' in model_name
  if is_panoptic:
    __SCREAMING_SNAKE_CASE      :	List[str]					=		250
  else:
    __SCREAMING_SNAKE_CASE      :	Tuple					=		91
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		'''huggingface/label-files'''
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		'''coco-detection-id2label.json'''
    __SCREAMING_SNAKE_CASE      :	Any					=		json.load(open(hf_hub_download(snake_case       ,			snake_case       ,			repo_type='''dataset'''							)       ,			'''r'''							)							)
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{int(snake_case							): v for k, v in idalabel.items()}
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		idalabel
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		{v: k for k, v in idalabel.items()}

  return config, is_panoptic


def 							a__					(							snake_case							):





  """simple docstring"""
  # here we list all keys to be renamed (original name on the left, our name on the right)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[]

  # stem
  # fmt: off
  rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')							)
  rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')							)
  rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')							)
  rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')							)
  rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')							)
  # stages
  for stage_idx in range(len(config.backbone_config.depths							)							):
    for layer_idx in range(config.backbone_config.depths[stage_idx]							):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
            )							)
      # 3 convs
      for i in range(3							):
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
            )							)
        rename_keys.append(
            (
                F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
            )							)
    # fmt: on

  for i in range(config.encoder_layers							):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (
            F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
            F'''encoder.layers.{i}.self_attn.out_proj.weight''',
        )							)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')							)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''')							)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''')							)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''')							)
    rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''')							)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')							)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''')							)
    rename_keys.append(
        (F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''')							)
    rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''')							)
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
            F'''decoder.layers.{i}.self_attn.out_proj.weight''',
        )							)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')							)
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
            F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
        )							)
    rename_keys.append(
        (
            F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
            F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
        )							)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''')							)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''')							)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''')							)
    rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''')							)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')							)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''')							)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')							)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')							)
    rename_keys.append(
        (F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''')							)
    rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''')							)

  # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
  rename_keys.extend(
      [
          ('''input_proj.weight''', '''input_projection.weight'''),
          ('''input_proj.bias''', '''input_projection.bias'''),
          ('''query_embed.weight''', '''query_position_embeddings.weight'''),
          ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
          ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
          ('''class_embed.weight''', '''class_labels_classifier.weight'''),
          ('''class_embed.bias''', '''class_labels_classifier.bias'''),
          ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
          ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
          ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
          ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
          ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
          ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
      ]							)

  return rename_keys


def 							a__					(							snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	int					=		state_dict.pop(snake_case							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		val


def 							a__					(							snake_case       ,			snake_case=False							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Dict					=		''''''
  if is_panoptic:
    __SCREAMING_SNAKE_CASE      :	Dict					=		'''detr.'''

  # first: transformer encoder
  for i in range(6							):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    __SCREAMING_SNAKE_CASE      :	Tuple					=		state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''							)
    # next, add query, keys and values (in that order) to the state dict
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		in_proj_weight[:256, :]
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		in_proj_bias[:256]
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		in_proj_weight[256:512, :]
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		in_proj_bias[256:512]
    __SCREAMING_SNAKE_CASE      :	Any					=		in_proj_weight[-256:, :]
    __SCREAMING_SNAKE_CASE      :	Any					=		in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6							):
    # read in weights + bias of input projection layer of self-attention
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
    __SCREAMING_SNAKE_CASE      :	int					=		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''							)
    # next, add query, keys and values (in that order) to the state dict
    __SCREAMING_SNAKE_CASE      :	Tuple					=		in_proj_weight[:256, :]
    __SCREAMING_SNAKE_CASE      :	int					=		in_proj_bias[:256]
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		in_proj_weight[256:512, :]
    __SCREAMING_SNAKE_CASE      :	Any					=		in_proj_bias[256:512]
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		in_proj_weight[-256:, :]
    __SCREAMING_SNAKE_CASE      :	List[str]					=		in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    __SCREAMING_SNAKE_CASE      :	Tuple					=		state_dict.pop(
        F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''							)
    __SCREAMING_SNAKE_CASE      :	Tuple					=		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''							)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    __SCREAMING_SNAKE_CASE      :	Tuple					=		in_proj_weight_cross_attn[:256, :]
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		in_proj_bias_cross_attn[:256]
    __SCREAMING_SNAKE_CASE      :	List[str]					=		in_proj_weight_cross_attn[256:512, :]
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		in_proj_bias_cross_attn[256:512]
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		in_proj_weight_cross_attn[-256:, :]
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		in_proj_bias_cross_attn[-256:]


def 							a__					(							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Tuple					=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		Image.open(requests.get(snake_case       ,			stream=snake_case							).raw							)

  return im


@torch.no_grad()
def 							a__					(							snake_case       ,			snake_case=None       ,			snake_case=False							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[Any]					=		get_detr_config(snake_case							)

  # load original model from torch hub
  __SCREAMING_SNAKE_CASE      :	List[str]					=		{
      '''detr-resnet-50''': '''detr_resnet50''',
      '''detr-resnet-101''': '''detr_resnet101''',
  }
  logger.info(F'''Converting model {model_name}...'''							)
  __SCREAMING_SNAKE_CASE      :	Any					=		torch.hub.load('''facebookresearch/detr'''       ,			model_name_to_original_name[model_name]       ,			pretrained=snake_case							).eval()
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		detr.state_dict()
  # rename keys
  for src, dest in create_rename_keys(snake_case							):
    if is_panoptic:
      __SCREAMING_SNAKE_CASE      :	List[str]					=		'''detr.''' + src
    rename_key(snake_case       ,			snake_case       ,			snake_case							)
  # query, key and value matrices need special treatment
  read_in_q_k_v(snake_case       ,			is_panoptic=snake_case							)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  __SCREAMING_SNAKE_CASE      :	str					=		'''detr.model.''' if is_panoptic else '''model.'''
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('''detr'''							)
          and not key.startswith('''class_labels_classifier'''							)
          and not key.startswith('''bbox_predictor'''							)
      ):
        __SCREAMING_SNAKE_CASE      :	Optional[int]					=		state_dict.pop(snake_case							)
        __SCREAMING_SNAKE_CASE      :	Any					=		val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        __SCREAMING_SNAKE_CASE      :	Optional[int]					=		state_dict.pop(snake_case							)
        __SCREAMING_SNAKE_CASE      :	str					=		val
      elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
        continue
      else:
        __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		state_dict.pop(snake_case							)
        __SCREAMING_SNAKE_CASE      :	List[str]					=		val
    else:
      if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
        __SCREAMING_SNAKE_CASE      :	str					=		state_dict.pop(snake_case							)
        __SCREAMING_SNAKE_CASE      :	Any					=		val

    # finally, create HuggingFace model and load state dict
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		DetrForSegmentation(snake_case							) if is_panoptic else DetrForObjectDetection(snake_case							)
  model.load_state_dict(snake_case							)
  model.eval()

  # verify our conversion on an image
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		'''coco_panoptic''' if is_panoptic else '''coco_detection'''
  __SCREAMING_SNAKE_CASE      :	List[str]					=		DetrImageProcessor(format=snake_case							)

  __SCREAMING_SNAKE_CASE      :	Tuple					=		processor(images=prepare_img()       ,			return_tensors='''pt'''							)
  __SCREAMING_SNAKE_CASE      :	str					=		encoding['''pixel_values''']

  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		detr(snake_case							)
  __SCREAMING_SNAKE_CASE      :	str					=		model(snake_case							)

  assert torch.allclose(outputs.logits       ,			original_outputs['''pred_logits''']       ,			atol=1E-3							)
  assert torch.allclose(outputs.pred_boxes       ,			original_outputs['''pred_boxes''']       ,			atol=1E-3							)
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks       ,			original_outputs['''pred_masks''']       ,			atol=1E-4							)
  print('''Looks ok!'''							)

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''							)
    Path(snake_case							).mkdir(exist_ok=snake_case							)
    model.save_pretrained(snake_case							)
    processor.save_pretrained(snake_case							)

  if push_to_hub:
    # Upload model and image processor to the hub
    logger.info('''Uploading PyTorch model and image processor to the hub...'''							)
    model.push_to_hub(F'''nielsr/{model_name}'''							)
    processor.push_to_hub(F'''nielsr/{model_name}'''							)


if __name__ == "__main__":
   lowercase_ 				=			argparse.ArgumentParser()

   parser.add_argument(
       """--model_name""",
       default="""detr-resnet-50""",
       type=str,
       choices=["""detr-resnet-50""", """detr-resnet-101"""],
       help="""Name of the DETR model you\'d like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
   )
   parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
   lowercase_ 				=			parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))